1. Field of the Invention
The present invention generally relates to assessing the severity of blood loss and predict the occurrence of hemorrhagic shock (HS) from biomedical signals and, more particularly, to combining the predictive capabilities of Transcranal Doppler (TCD) with Electrocardiogram (ECG) to predict hemorrhagic shock.
2. Background Description
Hemorrhage is the most severe factor in traumatic injuries and their critical care. Since hemorrhage can cause inadequate tissue perfusion and organ damage, a condition termed hemorrhage shock (HS) relies heavily on the early diagnosis and treatment (see, for example, C. J. Carrico, J. B. Holcomb, I. H. Chaudry, and PULSE trauma work group (Post Resuscitative and Initial Utility of Life Saving Efforts), “Scientific priorities and strategic planning for resuscitation research and life saving therapy following traumatic injury”, Academic Emergency Medicine, 2002, vol. 9, pp. 621-626, [2], and G. Gutierrez, H. D. Reines, and M. E. Wulf-Gutierrez, “Clinical review: Hemorrhagic shock”, Critical Care, 2004, vol, 8, pp. 373-381). Classifying the degree of severity of blood loss is vital in ensuring prompt treatment and a higher survival rate. Prompt detection and treatment of hemorrhagic injuries is also essential in the military field and for civilian trauma patients. Therefore, it is highly desirable to evaluate the severity of blood loss and predict the future occurrence of hemorrhagic shock (HS) by processing biomedical signals available in clinical settings.
Biological time series recognition analysis has been studied for many years to obtain significant information associated with diseases. For example, Electrocardiography (ECG) analysis has been shown to provide abnormal heart function information about autonomic control of the cardiovascular system, and so can explain a variety of cardiac dysfunctions (see, for example, Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, “Heart rate variability: standards of measurement, physiological interpretation and clinical use”, Circulation, 1996, vol. 93, pp. 1043-1065). By analyzing the physiological signal, an early diagnosis may be obtained. Even though, ECG combined with blood pressure (BP) is useful for analyzing cardiac activity, it may be insufficient for early estimation of hemorrhagic shock (see, for example, C. C. Wo, W. C. Shoemaker, P. L. Appel, M. H. Bishop, H. B. Kram, and E. Hardin, “Unreliability of blood pressure and heart rate to evaluate cardiac output in emergency resuscitation and critical illness”, Critical Care Medicine, 1993, vol. 21, pp. 218-223, S. A. Stern, S. C. Dronen, P. Birrer, X. Wang, “Effect of blood pressure on hemorrhage volume and survival in a near-fatal hemorrhage model incorporating a vascular injury”, Annals of Emergency Medicine, 1993, vol. 22, no. 2, pp. 155-63, and D. G. Newman, R. Callister, “The non-invasive assessment of stroke volume and cardiac output by impedance cardiography review”, Aviation, Space, and Environmental Medicine, 1999, vol. 70, pp. 780-789). Incorporating other physiological signals may therefore further improve such estimations.
Transcranial Doppler (TCD) ultrasound is a non-invasive medical monitoring method that is clinically used to examine the circulation of blood inside the human brain. During a typical TCD monitoring, ultrasound waves, which are transmitted through the tissues inside the skull, are reflected off the red blood cells moving along the blood vessels. Detection of these echoes allows estimation of the blood flow. The real-time use of TCD monitoring can also be used to monitor and record the blood flow inside the brain during a number of important surgical procedures. Measurement of blood flow can be used to assess flow deficits and to guide therapeutic interventions directed at optimizing cerebral blood flow (see, for example, V. L. Babikian, L. R. Wechsler, Transcranial Doppler Ultrasonography, Butterworth-Heinemann, Boston, 1999, and B. Bein, P. Meybohm, E. Cavus, P. H. Tonner, M. Steinfath, J. Scholz, and V. Doerges, “A comparison of transcranial Doppler with near infrared spectroscopy and indocyanine green during hemorrhagic shock: a prospective experimental study”, Critical Care, vol. 10, no. 1, 2006).
Many physiological time series are non-stationary, as they show very irregular and complex time-varying statistical patterns. Analyses of physiological signals commonly use Fourier transformation (FT). FT is known to be suitable for extracting frequency information from signals. However, it has a clear disadvantage when using non-stationary signal such as ECG and TCD and does not provide time information from the signal (see, for example, Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, “Heart rate variability: standards of measurement, physiological interpretation and clinical use”, Circulation, vol. 93, pp. 1043-1065, 1996). In particular, when time information has valuable knowledge such as HS due to assess the rapid change of response in hemorrhage; the FT cannot help to extract the time information. Therefore, wavelet transformation (WT) (see, for example, Mallat, A., Wavelet Tour of Signal Processing, Academic Press, San Diego, USA, 1998, R. M. Rao and A. S. Bopardikar, Wavelet Transforms Introduction to Theory and Applications, Addison Wesley Ed. 1998, C. S. Burrus, R. A. Gopinath, and H. Guo, Introduction to Wavelets and Wavelet Transforms, a Primer, Prentice Hall Inc. 1997) is applied to obtain the time-frequency information from the TCD and ECG. Wavelet transformation is a promising technique for extracting time-frequency information which is called multi-resolution analysis. Multi-resolution wavelet analysis has been widely applied to many fields, especially to biomedical signals, such as brain wave signal processing (see, for example, M. Kawase, T. Komatsu, U. Kondo, K. Nishiwaki, T. Kimura, and Y. Shimada, “Hemorrhage exerts different effects on variability of heart rate and blood pressure in dogs”, The Japanese Journal of Anesthesiology, vol. 47, pp. 925-932, 1998, and M. F. Hilton, R. A. Bates, K. R. Godfrey, M. J. Chappell, and R. M. Cayton, “Evaluation of frequency and time-frequency spectral analysis of heart rate variability as a diagnostic marker of the sleep apnoea syndrome”, Medical & Biological Engineering Computing, vol. 37, pp. 760-769, 1999) and ECG analysis (see, for example, X. M. Wu, R. J. Ceng, J. D. Liang, and H. Y. Li, “The study on the principle of wavelet analysis of the heart function parameters”, Journal of Jinan University, vol. 18, pp. 53-57, 1997).